1. Field of the Invention
The present invention relates generally to color matching formulation, and more particularly to artificial neural networks used in color matching formulation.
2. Description of Related Art
Vehicle paint laboratories perform paint color matching for numerous applications such as painting parts to match other painted parts and surfaces and painting portions of a vehicle that require body repair. Vehicle paint laboratories handle a great deal of complex information such as color measurement data and historical paint formulations. Numerous decisions are also made in color matching based on human analysis of color-related data. A large group of people, equipment and materials are needed to accomplish this task.
Presently, one color matching approach includes identifying the effect pigments with the aid of a microscope and measuring the color values of the standard. Effect pigments include compositions which influence the chromatic and reflective characteristics of the paint. Most notably, effect pigments lend a sparkle characteristic to the paint and may produce color travel. The term xe2x80x9ccolor travelxe2x80x9d denotes a color which changes with viewing angle.
Software is used to search databases of previous matches based on the data collected. In some cases, an existing formula may provide a close match. In other instances, the existing match can be utilized as a starting point for the color matching process. In such cases, software may provide a recommendation of correcting this formula. The corrected formula is mixed, sprayed, and compared to the standard. Further formula corrections are performed until the match is deemed suitably close. In some cases, the standard color is so unique that no existing match will serve as a suitable starting point. In these cases, the colors are xe2x80x9cmatched from scratch.xe2x80x9d Software is used to produce a best theoretical match of reflectance curves or other color attributes based on pigments selected by a color technician.
A deficiency in this lab process is the color matching correction process. Corrections are performed both with and without the aid of software. In general, too many corrections are performed, and they are often ineffective. Deciding how to correct a formula to better match a color standard is often the most difficult part of color matching. The manner in which the pigments interact with the light and one another to produce the color of the paint is complex. Often more than ten formula correction steps are needed to suitably match metallic or pearl colors. Years of training are required for a technician to learn the subtleties of these interactions and to become proficient at correcting formulas.
Traditional computer software that assists the technician in his tasks has several disadvantages. Traditional computer software has not proven to be very effective on colors containing xe2x80x9ceffect pigments.xe2x80x9d This software is typically based on a physical model of the interaction between illuminating light and the coating. These physical models involve complex physics and typically do not account for all aspects of the phenomena. A traditional approach is to use a model based on the work of Kubleka-Munk or modifications thereof. The model is difficult to employ with data obtained from multi-angle color measuring devices. One particular difficulty is handling specular reflection that occurs near the gloss angle. Another deficiency of the Kubleka-Munk based models is that only binary or ternary pigment mixtures are used to obtain the constants of the model. The model therefore may not properly account for the complexities of the multiple interactions prevalent in most paint recipes. The present invention overcomes these and other disadvantages. One particular advantage of this invention is that it allows for continual improvement. As new formulation and color measurement sets are created, they can be added to training sets for the neural networks. As the matching processes continue, the performance of the matching system can increase.
In accordance with the teachings of the present invention, a method and apparatus for color matching are provided that employ paint recipe neural networks. The color of a standard is expressed as color values. The neural network includes an input layer having nodes for receiving input data related to paint recipes. Weighted connections connect to the nodes of the input layer and have coefficients for weighting the input data. An output layer having nodes are either directly or indirectly connected to the weighted connections. The output layer generates output data that is related to the color attributes. The data of the input layer and the data from the output layer are interrelated through the neural network""s nonlinear relationship.
Additional advantages and aspects of the present invention will become apparent from the subsequent description and the appended claims, taken in conjunction with the accompanying drawings in which: